CNNSupervisedTrainer_VGG16.py 20.2 KB
 Nicola Gatto committed Apr 08, 2019 1 2 3 4 5 6 ``````import mxnet as mx import logging import numpy as np import time import os import shutil `````` Christian Fuß committed Oct 08, 2019 7 ``````import pickle `````` Sebastian N. committed Oct 18, 2019 8 9 ``````import math import sys `````` Nicola Gatto committed Apr 08, 2019 10 11 ``````from mxnet import gluon, autograd, nd `````` Eyüp Harputlu committed Jun 05, 2019 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 ``````class CrossEntropyLoss(gluon.loss.Loss): def __init__(self, axis=-1, sparse_label=True, weight=None, batch_axis=0, **kwargs): super(CrossEntropyLoss, self).__init__(weight, batch_axis, **kwargs) self._axis = axis self._sparse_label = sparse_label def hybrid_forward(self, F, pred, label, sample_weight=None): pred = F.log(pred) if self._sparse_label: loss = -F.pick(pred, label, axis=self._axis, keepdims=True) else: label = gluon.loss._reshape_like(F, label, pred) loss = -F.sum(pred * label, axis=self._axis, keepdims=True) loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True) `````` Eyüp Harputlu committed Jun 24, 2019 28 29 30 31 32 33 34 35 36 ``````class LogCoshLoss(gluon.loss.Loss): def __init__(self, weight=None, batch_axis=0, **kwargs): super(LogCoshLoss, self).__init__(weight, batch_axis, **kwargs) def hybrid_forward(self, F, pred, label, sample_weight=None): loss = F.log(F.cosh(pred - label)) loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True) `````` Sebastian N. committed Oct 30, 2019 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 ``````class SoftmaxCrossEntropyLossIgnoreIndices(gluon.loss.Loss): def __init__(self, axis=-1, ignore_indices=[], sparse_label=True, from_logits=False, weight=None, batch_axis=0, **kwargs): super(SoftmaxCrossEntropyLossIgnoreIndices, self).__init__(weight, batch_axis, **kwargs) self._axis = axis self._ignore_indices = ignore_indices self._sparse_label = sparse_label self._from_logits = from_logits def hybrid_forward(self, F, pred, label, sample_weight=None): log_softmax = F.log_softmax pick = F.pick if not self._from_logits: pred = log_softmax(pred, self._axis) if self._sparse_label: loss = -pick(pred, label, axis=self._axis, keepdims=True) else: label = _reshape_like(F, label, pred) loss = -(pred * label).sum(axis=self._axis, keepdims=True) # ignore some indices for loss, e.g. tokens in NLP applications for i in self._ignore_indices: `````` Christian Fuß committed Dec 18, 2019 57 `````` loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i) * mx.nd.equal(mx.nd.argmax(pred, axis=1), label)) `````` Sebastian N. committed Oct 30, 2019 58 59 `````` return loss.mean(axis=self._batch_axis, exclude=True) `````` Sebastian N. committed Oct 18, 2019 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 ``````@mx.metric.register class BLEU(mx.metric.EvalMetric): N = 4 def __init__(self, exclude=None, name='bleu', output_names=None, label_names=None): super(BLEU, self).__init__(name=name, output_names=output_names, label_names=label_names) self._exclude = exclude or [] self._match_counts = [0 for _ in range(self.N)] self._counts = [0 for _ in range(self.N)] self._size_ref = 0 self._size_hyp = 0 def update(self, labels, preds): labels, preds = mx.metric.check_label_shapes(labels, preds, True) new_labels = self._convert(labels) new_preds = self._convert(preds) for label, pred in zip(new_labels, new_preds): reference = [word for word in label if word not in self._exclude] hypothesis = [word for word in pred if word not in self._exclude] self._size_ref += len(reference) self._size_hyp += len(hypothesis) for n in range(self.N): reference_ngrams = self._get_ngrams(reference, n + 1) hypothesis_ngrams = self._get_ngrams(hypothesis, n + 1) match_count = 0 for ngram in hypothesis_ngrams: if ngram in reference_ngrams: reference_ngrams.remove(ngram) match_count += 1 self._match_counts[n] += match_count self._counts[n] += len(hypothesis_ngrams) def get(self): precisions = [sys.float_info.min for n in range(self.N)] i = 1 for n in range(self.N): match_counts = self._match_counts[n] counts = self._counts[n] if counts != 0: if match_counts == 0: i *= 2 match_counts = 1 / i `````` Christian Fuß committed Nov 12, 2019 117 118 `````` if (match_counts / counts) > 0: precisions[n] = match_counts / counts `````` Sebastian N. committed Oct 18, 2019 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 `````` bleu = self._get_brevity_penalty() * math.exp(sum(map(math.log, precisions)) / self.N) return (self.name, bleu) def calculate(self): precisions = [sys.float_info.min for n in range(self.N)] i = 1 for n in range(self.N): match_counts = self._match_counts[n] counts = self._counts[n] if counts != 0: if match_counts == 0: i *= 2 match_counts = 1 / i precisions[n] = match_counts / counts return self._get_brevity_penalty() * math.exp(sum(map(math.log, precisions)) / self.N) def _get_brevity_penalty(self): if self._size_hyp >= self._size_ref: return 1 else: return math.exp(1 - (self._size_ref / self._size_hyp)) @staticmethod def _get_ngrams(sentence, n): ngrams = [] if len(sentence) >= n: for i in range(len(sentence) - n + 1): ngrams.append(sentence[i:i+n]) return ngrams @staticmethod def _convert(nd_list): if len(nd_list) == 0: return [] new_list = [[] for _ in range(nd_list[0].shape[0])] for element in nd_list: for i in range(element.shape[0]): new_list[i].append(element[i].asscalar()) return new_list `````` Christian Fuß committed Oct 08, 2019 170 `````` `````` Sebastian N. committed Oct 30, 2019 171 172 `````` `````` Sebastian Nickels committed May 26, 2019 173 ``````class CNNSupervisedTrainer_VGG16: `````` Sebastian N. committed Jun 21, 2019 174 `````` def __init__(self, data_loader, net_constructor): `````` Nicola Gatto committed Apr 08, 2019 175 176 `````` self._data_loader = data_loader self._net_creator = net_constructor `````` Sebastian N. committed Jun 21, 2019 177 `````` self._networks = {} `````` Nicola Gatto committed Apr 08, 2019 178 179 180 181 `````` def train(self, batch_size=64, num_epoch=10, eval_metric='acc', `````` Sebastian N. committed Oct 18, 2019 182 `````` eval_metric_params={}, `````` Sebastian N. committed Dec 20, 2019 183 `````` eval_train=False, `````` Eyüp Harputlu committed Jun 05, 2019 184 185 `````` loss ='softmax_cross_entropy', loss_params={}, `````` Nicola Gatto committed Apr 08, 2019 186 187 188 189 `````` optimizer='adam', optimizer_params=(('learning_rate', 0.001),), load_checkpoint=True, checkpoint_period=5, `````` Sebastian N. committed Dec 20, 2019 190 191 `````` log_period=50, context='gpu', `````` Christian Fuß committed Nov 05, 2019 192 `````` save_attention_image=False, `````` 193 `````` use_teacher_forcing=False, `````` Nicola Gatto committed Apr 08, 2019 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 `````` normalize=True): if context == 'gpu': mx_context = mx.gpu() elif context == 'cpu': mx_context = mx.cpu() else: logging.error("Context argument is '" + context + "'. Only 'cpu' and 'gpu are valid arguments'.") if 'weight_decay' in optimizer_params: optimizer_params['wd'] = optimizer_params['weight_decay'] del optimizer_params['weight_decay'] if 'learning_rate_decay' in optimizer_params: min_learning_rate = 1e-08 if 'learning_rate_minimum' in optimizer_params: min_learning_rate = optimizer_params['learning_rate_minimum'] del optimizer_params['learning_rate_minimum'] optimizer_params['lr_scheduler'] = mx.lr_scheduler.FactorScheduler( optimizer_params['step_size'], factor=optimizer_params['learning_rate_decay'], stop_factor_lr=min_learning_rate) del optimizer_params['step_size'] del optimizer_params['learning_rate_decay'] `````` Sebastian N. committed Dec 20, 2019 217 `````` train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(batch_size) `````` Sebastian N. committed Jun 21, 2019 218 219 220 221 222 `````` if normalize: self._net_creator.construct(context=mx_context, data_mean=data_mean, data_std=data_std) else: self._net_creator.construct(context=mx_context) `````` Nicola Gatto committed Apr 08, 2019 223 224 225 226 227 228 229 230 `````` begin_epoch = 0 if load_checkpoint: begin_epoch = self._net_creator.load(mx_context) else: if os.path.isdir(self._net_creator._model_dir_): shutil.rmtree(self._net_creator._model_dir_) `````` Sebastian N. committed Jun 21, 2019 231 `````` self._networks = self._net_creator.networks `````` Nicola Gatto committed Apr 08, 2019 232 233 234 235 236 237 238 `````` try: os.makedirs(self._net_creator._model_dir_) except OSError: if not os.path.isdir(self._net_creator._model_dir_): raise `````` Sebastian N. committed Oct 31, 2019 239 `````` trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values() if len(network.collect_params().values()) != 0] `````` Nicola Gatto committed Apr 08, 2019 240 `````` `````` Eyüp Harputlu committed Jun 05, 2019 241 242 `````` margin = loss_params['margin'] if 'margin' in loss_params else 1.0 sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True `````` 243 `````` ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else [] `````` Eyüp Harputlu committed Jun 05, 2019 244 245 `````` if loss == 'softmax_cross_entropy': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False `````` 246 `````` loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel) `````` Christian Fuß committed Nov 26, 2019 247 `````` elif loss == 'softmax_cross_entropy_ignore_indices': `````` 248 `````` fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False `````` Christian Fuß committed Nov 05, 2019 249 `````` loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel) `````` Eyüp Harputlu committed Jun 05, 2019 250 `````` elif loss == 'sigmoid_binary_cross_entropy': `````` Nicola Gatto committed Apr 08, 2019 251 `````` loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss() `````` Eyüp Harputlu committed Jun 05, 2019 252 253 254 `````` elif loss == 'cross_entropy': loss_function = CrossEntropyLoss(sparse_label=sparseLabel) elif loss == 'l2': `````` Nicola Gatto committed Apr 08, 2019 255 `````` loss_function = mx.gluon.loss.L2Loss() `````` Eyüp Harputlu committed Jun 05, 2019 256 `````` elif loss == 'l1': `````` Nicola Gatto committed Apr 08, 2019 257 `````` loss_function = mx.gluon.loss.L2Loss() `````` Eyüp Harputlu committed Jun 05, 2019 258 259 260 261 262 263 264 265 266 267 268 269 270 `````` elif loss == 'huber': rho = loss_params['rho'] if 'rho' in loss_params else 1 loss_function = mx.gluon.loss.HuberLoss(rho=rho) elif loss == 'hinge': loss_function = mx.gluon.loss.HingeLoss(margin=margin) elif loss == 'squared_hinge': loss_function = mx.gluon.loss.SquaredHingeLoss(margin=margin) elif loss == 'logistic': labelFormat = loss_params['label_format'] if 'label_format' in loss_params else 'signed' loss_function = mx.gluon.loss.LogisticLoss(label_format=labelFormat) elif loss == 'kullback_leibler': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else True loss_function = mx.gluon.loss.KLDivLoss(from_logits=fromLogits) `````` Eyüp Harputlu committed Jun 24, 2019 271 272 `````` elif loss == 'log_cosh': loss_function = LogCoshLoss() `````` Eyüp Harputlu committed Jun 05, 2019 273 274 `````` else: logging.error("Invalid loss parameter.") `````` Nicola Gatto committed Apr 08, 2019 275 276 277 278 `````` tic = None for epoch in range(begin_epoch, begin_epoch + num_epoch): `````` Sebastian N. committed Dec 20, 2019 279 280 `````` loss_total = 0 `````` Nicola Gatto committed Apr 08, 2019 281 282 `````` train_iter.reset() for batch_i, batch in enumerate(train_iter): `````` Sebastian N. committed Nov 07, 2019 283 284 `````` with autograd.record(): labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed May 26, 2019 285 `````` `````` Sebastian N. committed Nov 07, 2019 286 287 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian N. committed Dec 20, 2019 288 `````` predictions_ = mx.nd.zeros((batch_size, 1000,), ctx=mx_context) `````` Christian Fuß committed Oct 08, 2019 289 `````` `````` Sebastian N. committed Aug 12, 2019 290 `````` `````` 291 292 `````` nd.waitall() `````` Christian Fuß committed Sep 10, 2019 293 `````` lossList = [] `````` Sebastian N. committed Nov 07, 2019 294 `````` `````` Sebastian N. committed Aug 12, 2019 295 `````` predictions_ = self._networks[0](data_) `````` Sebastian N. committed Nov 07, 2019 296 297 `````` lossList.append(loss_function(predictions_, labels[0])) `````` Sebastian Nickels committed Jun 06, 2019 298 `````` `````` Christian Fuß committed Sep 10, 2019 299 300 301 `````` loss = 0 for element in lossList: loss = loss + element `````` Nicola Gatto committed Apr 08, 2019 302 303 `````` loss.backward() `````` Sebastian N. committed Jun 21, 2019 304 `````` `````` Sebastian N. committed Dec 20, 2019 305 306 `````` loss_total += loss.sum().asscalar() `````` Sebastian N. committed Jun 21, 2019 307 308 `````` for trainer in trainers: trainer.step(batch_size) `````` Nicola Gatto committed Apr 08, 2019 309 310 311 312 `````` if tic is None: tic = time.time() else: `````` Sebastian N. committed Dec 20, 2019 313 `````` if batch_i % log_period == 0: `````` Nicola Gatto committed Apr 08, 2019 314 `````` try: `````` Sebastian N. committed Dec 20, 2019 315 `````` speed = log_period * batch_size / (time.time() - tic) `````` Nicola Gatto committed Apr 08, 2019 316 317 318 `````` except ZeroDivisionError: speed = float("inf") `````` Sebastian N. committed Dec 20, 2019 319 320 321 322 `````` loss_avg = loss_total / (batch_size * log_period) loss_total = 0 logging.info("Epoch[%d] Batch[%d] Speed: %.2f samples/sec Loss: %.5f" % (epoch, batch_i, speed, loss_avg)) `````` Nicola Gatto committed Apr 08, 2019 323 324 325 326 327 `````` tic = time.time() tic = None `````` Sebastian N. committed Dec 20, 2019 328 329 330 331 332 `````` if eval_train: train_iter.reset() metric = mx.metric.create(eval_metric, **eval_metric_params) for batch_i, batch in enumerate(train_iter): `````` Sebastian N. committed Nov 07, 2019 333 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed Jun 06, 2019 334 `````` `````` Sebastian N. committed Nov 07, 2019 335 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian Nickels committed Jun 06, 2019 336 `````` `````` Sebastian N. committed Dec 20, 2019 337 `````` predictions_ = mx.nd.zeros((batch_size, 1000,), ctx=mx_context) `````` Sebastian N. committed Aug 12, 2019 338 `````` `````` Sebastian Nickels committed May 26, 2019 339 `````` `````` 340 341 `````` nd.waitall() `````` Sebastian N. committed Nov 07, 2019 342 `````` outputs = [] `````` Christian Fuß committed Nov 05, 2019 343 `````` attentionList=[] `````` Sebastian N. committed Aug 12, 2019 344 `````` predictions_ = self._networks[0](data_) `````` Sebastian N. committed Nov 07, 2019 345 `````` `````` Christian Fuß committed Sep 23, 2019 346 `````` outputs.append(predictions_) `````` Sebastian Nickels committed May 26, 2019 347 `````` `````` Christian Fuß committed Nov 05, 2019 348 349 `````` if save_attention_image == "True": `````` Christian Fuß committed Dec 06, 2019 350 351 `````` import matplotlib matplotlib.use('Agg') `````` Christian Fuß committed Nov 05, 2019 352 353 354 355 356 357 358 `````` import matplotlib.pyplot as plt logging.getLogger('matplotlib').setLevel(logging.ERROR) if(os.path.isfile('src/test/resources/training_data/Show_attend_tell/dict.pkl')): with open('src/test/resources/training_data/Show_attend_tell/dict.pkl', 'rb') as f: dict = pickle.load(f) `````` Sebastian N. committed Dec 20, 2019 359 360 361 362 `````` plt.clf() fig = plt.figure(figsize=(15,15)) max_length = len(labels)-1 `````` 363 `````` ax = fig.add_subplot(max_length//3, max_length//4, 1) `````` Sebastian N. committed Dec 20, 2019 364 `````` ax.imshow(train_images[0+batch_size*(batch_i)].transpose(1,2,0)) `````` 365 `````` `````` Christian Fuß committed Nov 05, 2019 366 367 `````` for l in range(max_length): attention = attentionList[l] `````` Christian Fuß committed Nov 26, 2019 368 `````` attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1).squeeze() `````` Christian Fuß committed Nov 05, 2019 369 `````` attention_resized = np.resize(attention.asnumpy(), (8, 8)) `````` 370 `````` ax = fig.add_subplot(max_length//3, max_length//4, l+2) `````` Christian Fuß committed Nov 26, 2019 371 372 373 `````` if int(labels[l+1][0].asscalar()) > len(dict): ax.set_title("") elif dict[int(labels[l+1][0].asscalar())] == "": `````` 374 `````` ax.set_title(".") `````` Sebastian N. committed Dec 20, 2019 375 `````` img = ax.imshow(train_images[0+batch_size*(batch_i)].transpose(1,2,0)) `````` 376 377 378 379 `````` ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) break else: ax.set_title(dict[int(labels[l+1][0].asscalar())]) `````` Sebastian N. committed Dec 20, 2019 380 `````` img = ax.imshow(train_images[0+batch_size*(batch_i)].transpose(1,2,0)) `````` Christian Fuß committed Nov 26, 2019 381 `````` ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) `````` Christian Fuß committed Nov 05, 2019 382 383 384 385 `````` plt.tight_layout() target_dir = 'target/attention_images' if not os.path.exists(target_dir): `````` Christian Fuß committed Nov 26, 2019 386 `````` os.makedirs(target_dir) `````` Christian Fuß committed Nov 05, 2019 387 388 389 `````` plt.savefig(target_dir + '/attention_train.png') plt.close() `````` Sebastian N. committed Dec 20, 2019 390 391 392 393 394 395 `````` predictions = [] for output_name in outputs: if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: predictions.append(mx.nd.argmax(output_name, axis=1)) else: predictions.append(output_name) `````` Sebastian Nickels committed Jun 06, 2019 396 `````` `````` Sebastian N. committed Dec 20, 2019 397 398 399 400 `````` metric.update(preds=predictions, labels=labels) train_metric_score = metric.get()[1] else: train_metric_score = 0 `````` Nicola Gatto committed Apr 08, 2019 401 402 `````` test_iter.reset() `````` Sebastian N. committed Oct 18, 2019 403 `````` metric = mx.metric.create(eval_metric, **eval_metric_params) `````` Nicola Gatto committed Apr 08, 2019 404 `````` for batch_i, batch in enumerate(test_iter): `````` Sebastian N. committed Dec 20, 2019 405 `````` if True: `````` Sebastian N. committed Nov 07, 2019 406 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed Jun 06, 2019 407 `````` `````` Sebastian N. committed Nov 07, 2019 408 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian Nickels committed Jun 06, 2019 409 `````` `````` Sebastian N. committed Dec 20, 2019 410 `````` predictions_ = mx.nd.zeros((batch_size, 1000,), ctx=mx_context) `````` Sebastian N. committed Aug 12, 2019 411 `````` `````` Sebastian N. committed Jul 03, 2019 412 `````` `````` 413 414 `````` nd.waitall() `````` Sebastian N. committed Nov 07, 2019 415 `````` outputs = [] `````` Christian Fuß committed Nov 05, 2019 416 `````` attentionList=[] `````` Sebastian N. committed Aug 12, 2019 417 `````` predictions_ = self._networks[0](data_) `````` Sebastian N. committed Nov 07, 2019 418 `````` `````` Christian Fuß committed Sep 23, 2019 419 `````` outputs.append(predictions_) `````` Sebastian N. committed Jul 03, 2019 420 `````` `````` Christian Fuß committed Nov 05, 2019 421 422 `````` if save_attention_image == "True": `````` Sebastian N. committed Dec 20, 2019 423 424 425 426 427 428 429 430 431 432 `````` if not eval_train: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt logging.getLogger('matplotlib').setLevel(logging.ERROR) if(os.path.isfile('src/test/resources/training_data/Show_attend_tell/dict.pkl')): with open('src/test/resources/training_data/Show_attend_tell/dict.pkl', 'rb') as f: dict = pickle.load(f) `````` Christian Fuß committed Nov 05, 2019 433 `````` plt.clf() `````` 434 `````` fig = plt.figure(figsize=(15,15)) `````` Christian Fuß committed Nov 05, 2019 435 436 `````` max_length = len(labels)-1 `````` 437 `````` ax = fig.add_subplot(max_length//3, max_length//4, 1) `````` Sebastian N. committed Dec 20, 2019 438 `````` ax.imshow(test_images[0+batch_size*(batch_i)].transpose(1,2,0)) `````` 439 `````` `````` Christian Fuß committed Nov 05, 2019 440 441 `````` for l in range(max_length): attention = attentionList[l] `````` Christian Fuß committed Nov 26, 2019 442 `````` attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1).squeeze() `````` Christian Fuß committed Nov 05, 2019 443 `````` attention_resized = np.resize(attention.asnumpy(), (8, 8)) `````` 444 `````` ax = fig.add_subplot(max_length//3, max_length//4, l+2) `````` Christian Fuß committed Nov 26, 2019 445 446 447 `````` if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict): ax.set_title("") elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "": `````` 448 `````` ax.set_title(".") `````` Sebastian N. committed Dec 20, 2019 449 `````` img = ax.imshow(test_images[0+batch_size*(batch_i)].transpose(1,2,0)) `````` 450 451 452 `````` ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) break else: `````` Christian Fuß committed Nov 26, 2019 453 `````` ax.set_title(dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())]) `````` Sebastian N. committed Dec 20, 2019 454 `````` img = ax.imshow(test_images[0+batch_size*(batch_i)].transpose(1,2,0)) `````` Christian Fuß committed Nov 26, 2019 455 `````` ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) `````` Christian Fuß committed Nov 05, 2019 456 457 `````` plt.tight_layout() `````` Sebastian N. committed Dec 20, 2019 458 459 460 `````` target_dir = 'target/attention_images' if not os.path.exists(target_dir): os.makedirs(target_dir) `````` Christian Fuß committed Nov 05, 2019 461 462 463 `````` plt.savefig(target_dir + '/attention_test.png') plt.close() `````` Christian Fuß committed Sep 09, 2019 464 `````` predictions = [] `````` Christian Fuß committed Sep 23, 2019 465 `````` for output_name in outputs: `````` Sebastian N. committed Oct 30, 2019 466 `````` if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: `````` Christian Fuß committed Sep 09, 2019 467 468 469 470 `````` predictions.append(mx.nd.argmax(output_name, axis=1)) #ArgMax already applied else: predictions.append(output_name) `````` Sebastian Nickels committed May 26, 2019 471 `````` `````` Sebastian Nickels committed Jun 06, 2019 472 `````` metric.update(preds=predictions, labels=labels) `````` Nicola Gatto committed Apr 08, 2019 473 474 475 476 `````` test_metric_score = metric.get()[1] logging.info("Epoch[%d] Train: %f, Test: %f" % (epoch, train_metric_score, test_metric_score)) `````` Christian Fuß committed Aug 28, 2019 477 `````` `````` Nicola Gatto committed Apr 08, 2019 478 `````` if (epoch - begin_epoch) % checkpoint_period == 0: `````` Sebastian N. committed Jun 21, 2019 479 480 `````` for i, network in self._networks.items(): network.save_parameters(self.parameter_path(i) + '-' + str(epoch).zfill(4) + '.params') `````` Nicola Gatto committed Apr 08, 2019 481 `````` `````` Sebastian N. committed Jun 21, 2019 482 483 484 `````` for i, network in self._networks.items(): network.save_parameters(self.parameter_path(i) + '-' + str(num_epoch + begin_epoch).zfill(4) + '.params') network.export(self.parameter_path(i) + '_newest', epoch=0) `````` Nicola Gatto committed Apr 08, 2019 485 `````` `````` Sebastian N. committed Jun 21, 2019 486 `````` def parameter_path(self, index): `````` Bernhard Rumpe committed Aug 23, 2019 487 `` return self._net_creator._model_dir_ + self._net_creator._model_prefix_ + '_' + str(index)``